Exploring the Potential of Pretrained CNNs and Time-Frequency Methods for Accurate Epileptic EEG Classification: A Comparative Study.

Prompt diagnosis of epilepsy relies on accurate classification of automated electroencephalogram (EEG) signals. Several approaches have been developed to characterize epileptic EEG data; however, none of them have exploited time-frequency data to evaluate the effect of tweaking parameters in pretrained frameworks for EEG data classification. This study compares the performance of several pretrained convolutional neural networks (CNNs) namely, AlexNet, GoogLeNet, MobileNetV2, ResNet-18 and SqueezeNet for the localization of epilepsy EEG data using various time-frequency data representation algorithms. Continuous wavelet transform (CWT), empirical Fourier decomposition (EFD), empirical mode decomposition (EMD), empirical wavelet transform (EWT), and variational mode decomposition (VMD) were exploited for the acquisition of 2D scalograms from 1D data. The research evaluates the effect of multiple factors, including noisy versus denoised scalograms, different optimizers, learning rates, single versus dual channels, model size, and computational time consumption. The benchmark Bern Barcelona EEG dataset is used for testing purpose. Results obtained show that the combination of MobileNetV2, Continuous Wavelet Transform (CWT) and Adam optimizer at a learning rate of 10^-4, coupled with dual-data channels, provides the best performance metrics. Specifically, these parameters result in optimal sensitivity, specificity, f1-score, and classification accuracy, with respective values of 96.06%, 96.15%, 96.08%, and 96.10%. To further corroborate the efficacy of opted pretrained models on exploited Signal Decomposition (SD) algorithms, the classifiers are also being simulated on Temple University database at pinnacle modeling composition. A similar pattern in the outcome readily validate the findings of our study and robustness of deep learning models on epilepsy EEG scalograms.The conclusions drawn emphasize the potential of pretrained CNN-based models to create a robust, automated system for diagnosing epileptiform. Furthermore, the study offers insights into the effectiveness of varying time-frequency techniques and classifier parameters for classifying epileptic EEG data.

Multiscale denoising generative adversarial network for speckle reduction in optical coherence tomography images.

Purpose: Optical coherence tomography (OCT) is a noninvasive, high-resolution imaging modality capable of providing both cross-sectional and three-dimensional images of tissue microstructures. Owing to its low-coherence interferometry nature, however, OCT inevitably suffers from speckles, which diminish image quality and mitigate the precise disease diagnoses, and therefore, despeckling mechanisms are highly desired to alleviate the influences of speckles on OCT images. Approach: We propose a multiscale denoising generative adversarial network (MDGAN) for speckle reductions in OCT images. A cascade multiscale module is adopted as MDGAN basic block first to raise the network learning capability and take advantage of the multiscale context, and then a spatial attention mechanism is proposed to refine the denoised images. For enormous feature learning in OCT images, a deep back-projection layer is finally introduced to alternatively upscale and downscale the features map of MDGAN. Results: Experiments with two different OCT image datasets are conducted to verify the effectiveness of the proposed MDGAN scheme. Results compared those of the state-of-the-art existing methods show that MDGAN is able to improve both peak-single-to-noise ratio and signal-to-noise ratio by 3 dB at most, with its structural similarity index measurement and contrast-to-noise ratio being 1.4% and 1.3% lower than those of the best existing methods. Conclusions: Results demonstrate that MDGAN is effective and robust for OCT image speckle reductions and outperforms the best state-of-the-art denoising methods in different cases. It could help alleviate the influence of speckles in OCT images and improve OCT imaging-based diagnosis.

CGNet-assisted Automatic Vessel Segmentation for Optical Coherence Tomography Angiography.

Automatic optical coherence tomography angiography (OCTA) vessel segmentation is of great significance to retinal disease diagnoses. Due to the complex vascular structure, however, various existing factors make the segmentation task challenging. This paper reports a novel end-to-end three-stage channel and position attention (CPA) module integrated graph reasoning convolutional neural network (CGNet) for retinal OCTA vessel segmentation. Specifically, in the coarse stage, both CPA and graph reasoning network (GRN) modules are integrated in between a U-shaped neural network encoder and decoder to acquire vessel confidence maps. After being directed into a fine stage, such confidence maps are concatenated with the original image and the generated fine image map as a 3-channel image to refine retinal micro-vasculatures. Finally, both the fine and refined images are fused at the refining stage as the segmentation results. Experiments with different public datasets are conducted to verify the efficacy of the proposed CGNet. Results show that by employing the end-to-end training scheme and the integrated CPA and GRN modules, CGNet achieves 94.29% and 85.62% in area under the ROC curve (AUC) for the two different datasets, outperforming the state-of-the-art existing methods with both improved operability and reduced complexity in different cases. Code is available at https://github.com/GE-123-cpu/CGnet-for-vessel-segmentation.

Computerized Multidomain EEG Classification System: A New Paradigm.

The recent advancements in electroencepha- logram (EEG) signals classification largely center around the domain-specific solutions that hinder the algorithm cross-discipline adaptability. This study introduces a computer-aided broad learning EEG system (CABLES) for the classification of six distinct EEG domains under a unified sequential framework. Specifically, this paper proposes three novel modules namely, complex variational mode de- composition (CVMD), ensemble optimization-based featu- res selection (EOFS), and t-distributed stochastic neighbor embedding-based samples reduction (tSNE-SR) methods respectively for the realization of CABLES. Extensive expe- riments are carried out on seven different datasets from diverse disciplines using different variants of the neural network, extreme learning machine, and machine learning classifiers employing a 10-fold cross-validation strategy. Results compared with existing studies reveal that the highest classification accuracy of 99.1%, 97.8%, 94.3%, 91.5%, 98.9%, 95.3%, and 92% is achieved for the motor imagery dataset A, dataset B, slow cortical potentials, epilepsy, alcoholic, and schizophrenia EEG datasets res- pectively. The overall empirical analysis authenticates that the proposed CABLES framework outperforms the existing domain-specific methods in terms of classification accuracies and multirole adaptability, thus can be endorsed as an effective automated neural rehabilitation system.

Exploiting pretrained CNN models for the development of an EEG-based robust BCI framework.

Identifying motor and mental imagery electroencephalography (EEG) signals is imperative to realizing automated, robust brain-computer interface (BCI) systems. In the present study, we proposed a pretrained convolutional neural network (CNN)-based new automated framework feasible for robust BCI systems with small and ample samples of motor and mental imagery EEG training data. The framework is explored by investigating the implications of different limiting factors, such as learning rates and optimizers, processed versus unprocessed scalograms, and features derived from untuned pretrained models in small, medium, and large pretrained CNN models. The experiments were performed on three public datasets obtained from BCI Competition III. The datasets were denoised with multiscale principal component analysis, and time-frequency scalograms were obtained by employing a continuous wavelet transform. The scalograms were fed into several variants of ten pretrained models for feature extraction and identification of different EEG tasks. The experimental results showed that ShuffleNet yielded the maximum average classification accuracy of 99.52% using an RMSProp optimizer with a learning rate of 0.000 1. It was observed that low learning rates converge to more optimal performances compared to high learning rates. Moreover, noisy scalograms and features extracted from untuned networks resulted in slightly lower performance than denoised scalograms and tuned networks, respectively. The overall results suggest that pretrained models are robust when identifying EEG signals because of their ability to preserve the time-frequency structure of EEG signals and promising classification outcomes.

Identification of Motor and Mental Imagery EEG in Two and Multiclass Subject-Dependent Tasks Using Successive Decomposition Index.

The development of fast and robust brain-computer interface (BCI) systems requires non-complex and efficient computational tools. The modern procedures adopted for this purpose are complex which limits their use in practical applications. In this study, for the first time, and to the best of our knowledge, a successive decomposition index (SDI)-based feature extraction approach is utilized for the classification of motor and mental imagery electroencephalography (EEG) tasks. First of all, the public datasets IVa, IVb, and V from BCI competition III were denoised using multiscale principal analysis (MSPCA), and then a SDI feature was calculated corresponding to each trial of the data. Finally, six benchmark machine learning and neural network classifiers were used to evaluate the performance of the proposed method. All the experiments were performed for motor and mental imagery datasets in binary and multiclass applications using a 10-fold cross-validation method. Furthermore, computerized automatic detection of motor and mental imagery using SDI (CADMMI-SDI) is developed to describe the proposed approach practically. The experimental results suggest that the highest classification accuracy of 97.46% (Dataset IVa), 99.52% (Dataset IVb), and 99.33% (Dataset V) was obtained using feedforward neural network classifier. Moreover, a series of experiments, namely, statistical analysis, channels variation, classifier parameters variation, processed and unprocessed data, and computational complexity, were performed and it was concluded that SDI is robust for noise, and a non-complex and efficient biomarker for the development of fast and accurate motor and mental imagery BCI systems.